Modifiable Lifestyle and Medical Risk Factors Associated With Myeloproliferative Neoplasms
Bibliographic record
Abstract
Despite the identification of acquired genetic mutations associated with Myeloproliferative Neoplasms (MPNs) there is a paucity of information relating to modifiable risk factors that may lead to these mutations. The MOSAICC Study was an exploratory case-control study of polycythemia vera (PV), essential thrombocythemia (ET), and Myelofibrosis (MF). MPN patients and population controls (identified by General Practitioners) and non-blood relative/friend controls were recruited from 2 large UK centers. Participants completed a telephone-based questionnaire analyzed by unconditional logistic regression analysis adjusting for potential confounders. Risk factors for MPNs identified included increasing childhood household density [odds ratio (OR) 2.55, 95% confidence interval (CI) 1.16-5.62], low childhood socioeconomic status (OR 2.30, 95%CI 1.02-5.18) and high pack years smoking (OR 2.19, 95%CI 1.03-4.66) and current smoking restricted to JAK2 positive PV cases (OR 3.73, 95%CI 1.06-13.15). Obesity was linked with ET (OR 2.59, 95%CI 1.02-6.58) confirming results in previous cohort studies. Receipt of multiple CT scans was associated with a strongly increased risk of MPN although with wide confidence intervals (OR 5.38, 95%CI 1.67-17.3). Alcohol intake was inversely associated with risk of PV (OR 0.41, 95%CI 0.19-0.92) and ET (OR 0.48, 95%CI 0.24-0.98). The associations with childhood household density, high pack years smoking and alcohol were also seen in multivariate analysis. This is the largest case control study in MPNs to date and confirms the previously reported associations with obesity and cigarette smoking from cohort studies in addition to novel associations. In particular, the role of smoking and JAK2 mutation cases merits further evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".